Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
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Simply Coded Evolutionary Artificial Neural Networks on a Mobile Robot Control Problem
Yoshiaki KatadaTakuya Hidaka
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2010 Volume 23 Issue 1 Pages 1-8

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Abstract

One of the advantages of evolutionary robotics over other approaches in embodied cognitive science would be its parallel population search. Due to the population search, it takes a long time to evaluate all robot in a real environment. Thus, such techniques as to shorten the time are required for real robots to evolve in a real environment. This paper proposes to use simply coded evolutionary artificial neural networks for mobile robot control to make genetic search space as small as possible and investigates the performance of them using simulated and real robots. Two types of genetic algorithm (GA) are employed, one is the standard GA and the other is an extended GA, to achieve higher final fitnesses. The results suggest the benefits of the proposed method.

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© 2010 The Institute of Systems, Control and Information Engineers
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